12704

GPU Virtualization for High Performance General Purpose Computing on the ESX Hypervisor

Lan Vu, Hari Sivaraman, Rishi Bidarkar
University of Colorado Denver, 1380 Lawrence St., Denver, CO 80204, USA
High Performance Computing Symposium, 2014
@article{vu2014gpu,

   title={GPU Virtualization for High Performance General Purpose Computing on the ESX Hypervisor},

   author={Vu, Lan and Sivaraman, Hari and Bidarkar, Rishi},

   year={2014}

}

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Graphics Processing Units (GPU) have become important components in high performance computing (HPC) systems for their massively parallel computing capability and energy efficiency. Virtualization technologies are increasingly applied to HPC to reduce administration costs and improve system utilization. However, virtualizing the GPU to support general purpose computing presents many challenges because of the complexity of this device. On VMware’s ESX hypervisor, DirectPath I/O can provide virtual machines (VM) high performance access to physical GPUs. However, this technology does not allow multiplexing for sharing GPUs among VMs and is not compatible with vMotion, VMware’s technology for transparently migrating VMs among hosts inside clusters. In this paper, we address these issues by implementing a solution that uses "remote API execution" and takes advantage of DirectPath I/O to enable general purpose GPU on ESX. This solution, named vmCUDA, allows CUDA applications running concurrently in multiple VMs on ESX to share GPU(s). Our solution requires neither recompilation nor even editing of the source code of CUDA applications. Our performance evaluation has shown that vmCUDA introduced an overhead of 0.6% – 3.5% for applications with moderate data size and 14% – 20% for those with large data (e.g. 12.5 GB – 237.5GB in our experiments).
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